Quantitative DHI Analysis for Seismic Hydrocarbon Detection
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Solution Overview
Problem
Current methods for ranking hydrocarbon opportunities based on Direct Hydrocarbon Indicators (DHIs) in seismic data are subjective and qualitative, limiting the full potential of DHI analysis by relying on predefined sets of indicators rather than allowing all combinations to guide the identification of hydrocarbon opportunities.
Innovation Solution
A quantitative DHI definition system that uses algorithms to compute and evaluate DHIs across entire seismic data volumes, identifying hydrocarbon leads based on the presence and combinations of indicators, enabling systematic scanning and highlighting of prospective regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantitative algorithms are applied to compute and evaluate DHIs across entire seismic data volumes, then the accuracy and comprehensiveness of hydrocarbon opportunity identification is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The seismic data volume is divided into multiple smaller volumes or regions, and DHIs are computed and evaluated for each segment independently. This segmentation allows the complex quantitative analysis to be broken down into manageable units, reducing the computational complexity burden on any single processing node while maintaining comprehensive coverage of the entire data volume.
Solution Approach 2:
A computer system with specialized algorithms acts as an intermediary between the raw seismic data and the final hydrocarbon opportunity identification. The intermediary processes the seismic data through quantitative DHI computation and evaluation, transforming the complex raw data into structured assessments that can be systematically evaluated without requiring direct human interpretation of all complex parameters.
2Adaptability or versatility
If systematic scanning of entire seismic data volumes is performed to identify all possible DHI combinations, then the comprehensiveness of hydrocarbon lead identification is improved, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary computations of DHI indicators and their combinations across the entire seismic data volume before final hydrocarbon opportunity identification. By pre-computing and pre-evaluating DHI combinations systematically, the analysis prepares structured results in advance that can be quickly assessed and ranked, reducing the time required for final interpretation while maintaining comprehensive coverage of all possible indicator combinations.
3Ease of operation
If predefined sets of DHIs are used for qualitative assessment, then the ease of operation is maintained, but the adaptability to diverse geologic settings and DHI combinations is reduced
Solution Approach 1:
The DHI analysis system transitions from static predefined sets to dynamic, adaptive evaluation. The quantitative algorithms automatically adjust and evaluate DHI combinations based on the specific characteristics of each seismic data volume and geologic setting, allowing the system to adapt its assessment criteria dynamically rather than relying on fixed predefined sets, thereby maintaining ease of operation while significantly improving adaptability.
Data Source
AI summary
Method for automated and quantitative assessment of multiple direct hydrocarbon indicators (“DHI's”) extracted from seismic data. DHI's are defined in a quantitative way (33), making possible a method of geophysical prospecting based on quantification of DHI anomalies. Instead of working in a particular spatial region of seismic data pre-defined as a hydrocarbon opportunity, the present invention works on entire data volumes derived from the measured seismic data (31), and identifies opportunities based on quantified DHI responses. In some embodiments, a series of algorithms utilizes the geophysical responses that cause DHI's to arise in seismic data to search entire data sets and identify hydrocarbon leads based on the presence of individual and/or combinations of DHI's (34).